Electronic care and content clothing label

ABSTRACT

The technology disclosed here encodes a clothing item ID as an alphanumeric code within the clothing item, such as within a radio-frequency identification tag. A clothing item cleaning appliance, such as a cell phone, or a washing machine reads the clothing item ID from the clothing item, and retrieves the care and content information from a database maintained on a cloud. For example, the care and content information can include operating settings of the washing machine. The washing machine can adjust its settings and wash the clothing item in accordance with the care and content information of the clothing item. The care and content information stored in the database can be organized in optimized data structures enabling efficient responses to received queries, and efficient updates to the information stored in the data structures.

CROSS-REFERENCE TO RELATED APPLICATIONS

This application is a continuation of the U.S. utility patentapplication Ser. No. 15/484,998 filed Apr. 11, 2017, which claimspriority to the U.S. Provisional Patent Application Ser. No. 62/433,111filed Dec. 12, 2016, all of which are incorporated herein by thisreference in their entirety.

TECHNICAL FIELD

The present application is related to a clothing item care and contentlabels, and more specifically to methods and systems to electronicallyencode care and content information into a clothing item.

BACKGROUND

Today, clothing item manufacturers and apparel companies use millions ofmiles of ribbon made from unsustainable materials to create a label forvarious clothing items such as garments, shoes, hats, purses, etc. Theclothing item label must be large enough to contain all care and contentinformation for the garment. In addition, the clothing item label mustbe understood by users across the globe. Consequently, the clothing itemmanufacturers and apparel companies create label booklets in variouslanguages, thus further increasing the consumption of the unsustainablematerials.

Various manufacturers and apparel companies do not communicate amongeach other the care and content information even for products that areidentical, thus slowing down the process of authoring and/or printingcare and content labels. In addition, when the clothing itemmanufacturers and/or apparel companies make a mistake in authoringand/or printing of the clothing item label, fixing the mistake caninvolve recalls of hundreds of thousands of clothing items. As a result,the process of creating clothing item labels is inefficient in use ofunsustainable materials, prone to error, slow, and cost intensive.

SUMMARY

The technology disclosed here eliminates the need for printing complexcare and content information on clothing item labels, and translatingthe care and content information into various languages. Instead ofprinting the care and content information on the clothing item labels,the technology disclosed here encodes a clothing item ID as analphanumeric code within the clothing item, such as within aradio-frequency identification tag. A cell phone, or a washing machinereads the clothing item ID from the clothing item, and retrieves thecare and content information from a database maintained on a cloud. Forexample, the care and content information can include operating settingsof the washing machine. The washing machine can adjust its settings andwash the clothing item in accordance with the care and contentinformation of the clothing item. The care and content informationstored in the database can be organized in optimized data structuresenabling efficient responses to received queries, and efficient updatesto the information stored in the data structures.

In addition, the cell phone, or the washing machine can organize auser's wardrobe according to the washing instructions, and/or the dryinginstructions of the various clothing items in the user's wardrobe, anddisplay that information to the user, thus relieving the user of theneed to manually sort items according to the washing, and/or dryinginstructions. The information presented to the user or can be translatedinto a language that the user can understand. For example, theinformation can be translated into a language that the user hasspecified as the user's preferred language, or the information can betranslated into a language associated with a geolocation of the cellphone or the washing machine.

When care and content information for the clothing item needs to beupdated, instead of a costly recall involving hundreds of thousands ofclothing items, only the cloud database receives the update. When aquery is received for care and content information associated with aparticular clothing item ID, the database provides the updated care andcontent information.

As a result, the use of non-sustainable materials in printing clothingitem labels can be reduced by at least a 50%. The accuracy of the careand content information is increased because of frequent updates andefficient error correction of the care and content information. Thetransport of clothing items across borders is faster because theautomated checking of the clothing item contents enables fasterinspection of the transported clothing items. In addition, visuallyimpaired users can use clothing item cleaning appliances, because theclothing item cleaning appliances can operate automatically based on theretrieved care and content information. Finally, apparel companies candirectly communicate with their customers, and can recycle theirproducts to lower the clothing item waste around the globe.

BRIEF DESCRIPTION OF THE DRAWINGS

FIG. 1 shows a system for minimizing consumption of textile inproduction of clothing item labels.

FIG. 2 shows a clothing item with a clothing item ID encoded within theclothing item.

FIG. 3 shows a communication system between the clothing item, one ormore devices, and the cloud system, according to one embodiment.

FIG. 4 shows a communication system between the clothing item, one ormore devices, and the cloud system, according to another embodiment.

FIG. 5 shows an example of care and content information displayed on adevice of a user.

FIG. 6 shows a clothing item cleaning appliance reporting an error.

FIG. 7 is an example of organizing care and content information intobins.

FIG. 8 is a flowchart of a method to operate a clothing item cleaningappliance in accordance with care and content information of a clothingitem.

FIG. 9 is a system to operate a clothing item cleaning applianceaccording to a care and content information of the clothing item.

FIG. 10 is a flowchart of a method to minimize consumption of textileused in manufacturing of a clothing item label.

FIG. 11 is a system to encode clothing item ID within a clothing item.

FIG. 12 is a flowchart of a method to increase accuracy of a care andcontent information associated with a clothing item label.

FIG. 13 shows a data structure storing clothing item information.

FIG. 14 shows a data structure storing user information.

FIGS. 15A-15B show various meta-data structures.

FIG. 16 is a flowchart of a method to efficiently respond to a queryregarding a clothing item.

FIG. 17 is a flowchart of a method to enable efficient updates to careand content information of a clothing item.

FIG. 18 is a flowchart of a method to create a system to efficientlyrespond to an anticipated query.

FIG. 19 is a flowchart of a method to create a system to efficientlyupdate care and content information of a clothing item.

FIG. 20 is a diagrammatic representation of a machine in the exampleform of a computer system within which a set of instructions, forcausing the machine to perform any one or more of the methodologies ormodules discussed herein, may be executed.

DETAILED DESCRIPTION Technology

The technology disclosed here eliminates the need for printing complexcare and content information on clothing item labels, and translatingthe care and content information into various languages. Instead ofprinting the care and content information on the clothing item labels,the technology disclosed here encodes a clothing item ID as analphanumeric code within the clothing item, such as within aradio-frequency identification tag. A cell phone, or a washing machinereads the clothing item ID from the clothing item, and retrieves thecare and content information from a database maintained on a cloud. Forexample, the care and content information can include operating settingsof the washing machine. The washing machine can adjust its settings andwash the clothing item in accordance with the care and contentinformation of the clothing item. The care and content informationstored in the database can be organized in optimized data structuresenabling efficient responses to received queries, and efficient updatesto the information stored in the data structures.

In addition, the cell phone, or the washing machine can organize auser's wardrobe according to the washing instructions, and/or the dryinginstructions of the various clothing items in the user's wardrobe, anddisplay that information to the user, thus relieving the user of theneed to manually sort items according to the washing, and/or dryinginstructions. The information presented to the user or can be translatedinto a language that the user can understand. For example, theinformation can be translated into a language that the user hasspecified as the user's preferred language, or the information can betranslated into a language associated with a geolocation of the cellphone or the washing machine.

When care and content information for the clothing item needs to beupdated, instead of a costly recall involving hundreds of thousands ofclothing items, only the cloud database receives the update. When aquery is received for care and content information associated with aparticular clothing item ID, the database provides the updated care andcontent information.

As a result, the use of non-sustainable materials in printing clothingitem labels can be reduced by at least a 50%. The accuracy of the careand content information is increased because of frequent updates andefficient error correction of the care and content information. Thetransport of clothing items across borders is faster because theautomated checking of the clothing item contents enables fasterinspection of the transported clothing items. In addition, visuallyimpaired users can use clothing item cleaning appliances, because theclothing item cleaning appliances can operate automatically based on theretrieved care and content information. Finally, apparel companies candirectly communicate with their customers, and can recycle theirproducts to lower the clothing item waste around the globe.

FIG. 1 shows a system for minimizing consumption of textile inproduction of clothing item labels. The system 100 includes a cloudsystem 110, a communication network 120, administrators 130, and users140. Both administrators 130 and users 140 can be users of the system.The cloud system 110 receives through the communication network 120various information regarding care and content of various clothingitems. The cloud system 110 can be located on a server, a desktopcomputer, laptop computer, a cloud computer, etc. The communicationnetwork 120 can be a local area network (LAN), metropolitan area network(MAN), a wide area network (WAN), a public data network (e.g., theInternet), short range wireless network, a cellular network, etc.

Administrators 130 can be clothing item manufacturers, and/or designers.Administrators 130 can input and store care and content information foreach clothing item on the cloud system 110, along with an identification(ID) for each clothing item. The care and content information includesinformation regarding materials contained in the clothing item,percentage of each material contained in the clothing item, and washing,drying, ironing instructions for each material. For example, contentinformation can include percentage of each material contained in theclothing item and can specify “80% percent cotton, 20% polyester.”Additionally, the content information can include size of the clothingitem, size chart, color of the item, place of manufacturing of theclothing item, brand of the clothing item, etc. For example, the careinformation (i.e., care instructions) can include the following:“Machine wash cold. Wash with like colors. Do not bleach. Tumble drylow. Warm iron if necessary.” The care information can also include bestbrands of cleaning agents, such as best brand of a detergent, a bleach,a softener, etc., to use for the clothing item.

The clothing item ID can be an alphanumeric string that uniquelyidentifies a single clothing item. Alternatively, the identification canbe an alphanumeric string that uniquely identifies all clothing itemsmade by the same brand and/or apparel company, identical to each otherexcept varying in size. In addition, the identification can be analphanumeric string that uniquely identifies all clothing items thathave the same material content in terms of percentage. The clothing itemID can also be encoded within a logo of the clothing item. For example,small variations in the logo, imperceptible to the human eye, butmachine-readable, can encode the clothing item ID.

The administrators 130 can dynamically update the care information forany clothing item, even after the sale of the clothing item. Forexample, when the administrators 130 discover a method to wash theclothing item such that the longevity of the clothing item is extended,the administrators 130 can update the care and content information onthe cloud system 110, even after the clothing item has been sold.

The users 140 can be the buyers of the clothing item or professionalcleaners such as dry cleaners. The clothing item ID is stored within theclothing item, and a user device such as a mobile device, or a clothingitem cleaning appliance can read the clothing item ID. Upon reading theclothing item ID, the user device can query the cloud system 110 for thecare and content information of the clothing item. Once the user devicereceives the care and content information for the clothing item, theclothing item cleaning appliance can proceed in operating according tothe care and content information.

FIG. 2 shows a clothing item with a clothing item ID encoded within theclothing item. The clothing item 200 is a garment, however, the clothingitem 200 can be a footwear, a purse, a backpack, a hat, a cap, a hairaccessory, a sling, a baby carrier, and/or any item capable of beingworn by a person. The clothing item 200 can have the clothing item IDrecorded within the clothing item. For example, the clothing item ID canbe recorded in a radio-frequency identification (RFID) tag, a near fieldcommunication (NFC) tag, a quick response (QR) code, a barcode, aBluetooth transmitter, a wireless frequency (Wi-Fi) transmitter, or aglobal positioning system (GPS) transmitter, etc. The clothing item IDso recorded can be affixed on the clothing item label 210, on theclothing item tag 220, or within the fabric of the clothing item 200,etc. In one embodiment, the clothing item ID can be recorded in ane-textile that is a part of the clothing item fabric. E-textiles, alsoknown as smart clothing items, smart clothing, electronic textiles,smart textiles, or smart fabrics, are fabrics that enable digitalcomponents (including small computers), and electronics to be embeddedin them. The digital component carries the clothing item ID encodedwithin. The digital component can be an RFID tag, an NFC tag, aBluetooth transmitter, a Wi-Fi transmitter, a GPS transmitter, etc. Thedigital component included in the e-textile, affixed to the clothingitem label, or affixed to the clothing item itself, can be coated inpolyurethane to protect the digital component from moisture and heatwhen the clothing item is washed, dried, ironed, etc.

FIG. 3 shows a communication system between the clothing item, one ormore devices, and the cloud system, according to one embodiment. Thecommunication system includes a clothing item ID 330 encoded within theclothing item 300, one or more devices 310, 320, and the cloud system110. In this example, one or more devices 310, 320 include a user device310, and a clothing item cleaning appliance 320. The clothing itemcleaning appliance 320 can be a washing machine, a drying machine, a drycleaning appliance such as Swash, an ironing appliance, a steamingappliance, etc. The user device 310 can be a cell phone, a tablet, or adevice configured to read the clothing item ID, such as an RFID reader,NFC reader, barcode reader, etc.

The user device 310 reads the clothing item ID 330 from the clothingitem 300, and queries the cloud system 110 for care and contentinformation associated with the clothing item ID 330. Upon receiving thecare and content information from the cloud system 110, the user device310 communicates the care and content information to the clothing itemcleaning appliance 320. The clothing item cleaning appliance 320 setsits operation parameters to match the care and content informationreceived from the user device 310. When there are multiple items withconflicting care and content information placed in the clothing itemcleaning appliance 320, the clothing item cleaning appliance 320, or theuser device 310 can display an error, as described with respect to FIG.6.

For example, the care and content information can specify “machine washcold. Wash with like colors. Do not bleach.” When the clothing itemcleaning appliance 320 is a washing machine, the washing machine 320sets the wash cycle to cold. In another example, the care and contentinformation can specify “tumble dry low.” When the clothing itemcleaning appliance 320 is a dryer, the dryer 320 sets the dry cycle to“tumble dry low.”

The user device 310 can also display the care and content informationreceived from the cloud system 110 to a user of the user device 310.Based on the geolocation of the user device 310, or based on userpreferences stored in the user device 310, the care and contentinformation can be translated into a language that the user understands.

In one embodiment, the cloud system 110 receives the geolocation of theuser device 310, and based on the geolocation of the user device 310,the cloud system 110 translates the care and content information intothe language associated with the geolocation of the user device 310. Inanother embodiment, the cloud system 110 receives the user preferencesstored in the user device 310, such as the user's language preference.Based on the user's language preference, the cloud system 110 translatesthe care and content information into the appropriate language.Similarly, the translation can be performed on the user device 310,instead of the cloud system 110. When the user's language preference isspecified, the user's language preference overrides the geolocation ofthe user device 310. For example, if the user device 310 is located inUnited States, however the user preferences specify “Spanish” as theuser's language preference, the user device 310 displays the care andcontent information in Spanish.

FIG. 4 shows a communication system between the clothing item, one ormore devices, and the cloud system, according to another embodiment. Thecommunication system includes a clothing item ID 330 encoded within theclothing item 300, one or more devices 400, 320, and the cloud system110. The communication system in FIG. 4 is similar to the communicationsystem in FIG. 3. However, unlike in FIG. 3, in this example, the devicecommunicating with the cloud system 110 is the clothing item cleaningappliance 400. The clothing item cleaning appliance 400 can be a washingmachine, a drying machine, a dry cleaning appliance such as Swash, anironing appliance, a steaming appliance, etc.

The clothing item cleaning appliance 400, in addition to performingclothing item cleaning appliance functions as described in FIG. 3,performs all the functions of the user device 310, as described in FIG.3. For example, the clothing item cleaning appliance 400 can communicateits geolocation to the cloud system 110, can store user specifiedlanguage preference and communicate the user-specified languagepreference to the cloud system 110. The clothing item cleaning appliance400 can contain a display to show care and content information receivedfrom the cloud system 110 to the user.

The clothing item cleaning appliance 400 can include one or more readers410, 420, 430 to obtain the clothing item ID 330 encoded within theclothing item 300. One or more readers 410, 420, 430 can be an RFID tagreader, an NFC tag reader, a camera, a Bluetooth transceiver, a GPStransceiver, and/or a Wi-Fi transceiver, etc. The one or more readers410, 420, 430 can be placed outside the clothing item cleaning appliance400, or can be placed inside the clothing item cleaning appliance 400.

FIG. 5 shows an example of care and content information displayed on adevice of a user. The device 500 can be a user device, such as a cellphone, a tablet, etc. The device 500 can read the clothing item ID 510,and retrieve care and content information from the cloud system 110. Thecare and content information can be shown to the user on the display 530of the device 500. The care and content information can include size ofthe item, washing instructions, designer of the item, materials used inthe item, geolocation of manufacturing the item, size chart used indetermining the size of the item, color of the item, etc. Suchinformation can be useful to a consumer considering whether to buy theclothing item 520. The information displayed on the device 500 can betranslated into the language that the consumer can understand. Thetranslation can be based on the language associated with the geolocationof the device 500, or the translation can be based on the user'slanguage preference stored in the device 500, as described in thisapplication.

FIG. 6 shows a clothing item cleaning appliance reporting an error. Theclothing item cleaning appliance 600 can be a washing machine, dryingmachine, a dry cleaning appliance such as a Swash appliance, an ironingappliance, a steaming appliance, etc. The clothing item cleaningappliance can include one or more clothing item ID readers 610, 620which can read clothing item clothing item IDs placed within theclothing item cleaning appliance 600. The clothing item ID readers 610,620 can be an RFID tag reader, an NFC tag reader, a camera, a Bluetoothtransceiver, a GPS transceiver, and/or a Wi-Fi transceiver, etc., andcan be placed inside or outside of the clothing item cleaning appliance600. Once the clothing items 630, 640 have been placed inside theclothing item cleaning appliance 600, the clothing item cleaningappliance 600 can obtain the care information from a cloud system. Theclothing item cleaning appliance 600 can obtain the care information bydirectly communicating with the cloud system as described in FIG. 4, orby communicating with a user device 650 which in turn communicates withthe cloud system as described in FIG. 3.

The clothing item cleaning appliance 600, or the user device 650, canreceive care and content information from the cloud system for each ofthe clothing items placed inside the clothing item cleaning appliance600. The clothing item cleaning appliance 600, or the user device 650can compare the received care and content information to determine ifthe received care and content information for each of the clothing items630, 640 inside the clothing item cleaning appliance 600 is the same.When the received care content information for each of the clothing item630, 640 is not the same, the clothing item cleaning appliance 600 orthe user device 650 can report an error to a user. The error can bedisplayed on a display of the clothing item cleaning appliance 600, orthe display of the user device 650. In one embodiment, the user canoverride the error, and manually specify settings according to which theclothing item cleaning appliance 600 should operate. In anotherembodiment, the user can override the error, and let the clothing itemcleaning appliance 600 select the operating settings. For example, theclothing item cleaning appliance 600 can choose the gentlest operatingsetting, out of all received operating settings.

For example, when the care and content information for garment 630specifies “machine wash cold”, while the care and content informationfor garment 640 specifies “dry clean only,” the clothing item cleaningappliance 600 or the user device 650 can display an error such as “dryclean only: please remove the Donna Karan Cashmere sweater.” Clothingitem cleaning appliance 600 or the user device 650 receives the care andcontent information that garment 640 is a Cashmere sweater, that thegarment 640 is dry-clean only, and that the garment 640 is made by DonnaKaran. If the user overrides the reported error, the clothing itemcleaning appliance 600 or the user device 650 can choose the gentlestoperating settings. In the present case, if “dry clean” setting is notavailable, and the gentlest operating setting is “hand wash,” theclothing item cleaning appliance 600 proceeds to operate under “handwash” settings. In another example, the care and content information forgarment 630 can specify “machine wash hot,” while the care and contentinformation for garment 640 can specify “machine wash warm.” In thatcase, the clothing item cleaning appliance or the user device 650 canchoose the gentlest operating settings, which in this case are “machinewash warm.”

In another example, when the care and content information for garment630 specifies “machine wash cold. Wash with like colors. Color red”,while the care and contact information for garment 640 specifies“Machine wash cold. Color green”, the clothing item cleaning appliance600 or the user device 650 can display an error such as “differentcolors: please remove all non-red items.” If the user overrides thereported error, the clothing item cleaning appliance 600 or the userdevice 650 can choose the gentlest operating setting, such as “handwash”, to avoid color bleeding.

Similarly, when the clothing item cleaning appliance 600 is a dryer, oran ironing appliance, the clothing item cleaning appliance 600 canreport an error when the settings of the clothing item cleaningappliance 600 do not match the care and content information receivedfrom the cloud system.

FIG. 7 is an example of organizing care and content information intobins. The user device 700 obtains clothing item ID 710, from theclothing item 720. In one embodiment, the user device 700 sends theclothing item ID 710, a user identification (user ID), geolocation ofthe user device 700, and/or the user's language preference to a cloudsystem 110. The cloud system 110 can associate the clothing item ID 710with the user ID, and can create a digital wardrobe associated with theuser ID. The digital wardrobe can be organized into bins according tocare and content instructions. For example, a bin contains all of theclothing item IDs that have the same care information for a particularclothing item cleaning appliance. In a more specific example, the bincontains all the clothing item IDs that have the same wash instructions;or the bin can contain all the clothing item IDs that have the same dryinstructions; or the bin can contain all the clothing item IDs that havethe same ironing instructions. In FIG. 7, display 730 of the user device700 shows bins that contain all the clothing item IDs that have the samewash instructions. Based on the geolocation of the user device 700,and/or the user's language preference, the user device 700 shows bins ina language that the user can understand. In another embodiment, aclothing item cleaning appliance organizes the clothing item IDs intobins, and displays them to the user in the language of the user canunderstand.

FIG. 8 is a flowchart of a method to operate a clothing item cleaningappliance in accordance with care and content information of a clothingitem. In step 800, a processor obtains information encoded in a clothingitem. The information includes an identification (ID) of the clothingitem. The information can be encoded in the clothing item in variousways, such as a radio-frequency identification (RFID) tag, a near fieldcommunication (NFC) tag, a quick response (QR) code, a barcode, aBluetooth transmitter, a Wi-Fi transmitter, a GPS transmitter, etc. Theinformation can be encoded in an e-textile, or smart clothing, as adigital component embedded in a fabric of the clothing item. Theinformation can be affixed to the care content label, can be printedonto the clothing item, or woven into the clothing item as a fabric, athread, a button, a bead, etc., containing the digital component.

In step 810, the processor retrieves from a database care and contentinformation of the clothing item. The care and content information isassociated with the clothing item ID in the database. The care andcontent information can be washing instructions, drying instructions,ironing instructions, etc. The care and content information can includerecommendations regarding which cleaning agents to use, such as whichdetergent, bleach, or fabrics softeners are best.

In step 820, the processor automatically groups the clothing item withmultiple clothing items having care and content informationcorresponding to the care and content information of the clothing item,e.g., having the same care and content information. For example, inautomatically grouping the multiple clothing items, the processor candetect that multiple clothing items placed inside the clothing itemcleaning appliance have disparate care and content information. Upondetecting disparate care and content information, the processor can senda notification, which includes an indication that multiple clothingitems placed inside the clothing item cleaning appliance have disparatecare and content information. The notification can be an error code, ortext, displayed on the user device, or on the clothing item cleaningappliance. The text can be translated into a user preferred language, asdescribed in this application. The processor can allow the user tooverride the error, and force the operation of the clothing itemcleaning appliance. The operating settings of the clothing item cleaningappliance can be set by the user, or the clothing item cleaningappliance can select gentlest settings out of the disparate care andcontent information.

In another example, in automatically grouping the clothing items, theprocessor can check whether the capacity of the clothing item cleaningappliance has been exceeded once all the clothing items have been placedinto the clothing item cleaning appliance. The processor can send anotification which includes an indication that the capacity of theclothing item cleaning appliance has been exceeded. The notification canbe an error code, or text, displayed on the user device, or on theclothing item cleaning appliance. The text can be translated into a userpreferred language, as described in this application. The notificationcan also include a suggestion of how many clothing items to remove, orwhich specific clothing items to remove. In a more specific example, thenotification can say “please remove the Donna Karan sweater, and theAbercrombie and Fitch blouse,” or the notification can say “pleaseremove two clothing items from the clothing item cleaning appliance.”

In a third example, in automatically grouping the clothing items, theprocessor can organize multiple clothing items belonging to the userinto multiple bins. Each bin among multiple bins includes clothing itemsthat have the same care and content information. The processor candisplay the information contained in multiple bins to the user, in theuser's language preference, as described in this application.

In step 830, the processor operates a clothing item cleaning appliancein accordance with the care and content information retrieved in step810. The clothing item cleaning appliance can be a washing machine, adrying machine, a dry cleaning machine such as Swash, an ironingappliance, a steaming appliance, etc. In determining the operatingsettings of the clothing item cleaning appliance, the processor canadjust the operating settings to be the optimal settings for theclothing items inside the clothing item cleaning appliance. The optimalsettings may not be available to a user operating the clothing itemcleaning appliance manually. For example, the processor detects that theclothing items placed within a washer are 100% cotton. The processorretrieves from the database that the optimal settings for a washingmachine washing cotton are 108° C., and cycle length 30 minutes. Eventhough settings of 108° C. and cycle length 30 minutes are not availableas a button on the washing machine, the washing machine can proceed inwashing the clothing items according to the optimal settings for 100%cotton.

Upon completing the operation of the clothing item cleaning appliance,the processor can send to the database settings of the clothing itemcleaning appliance during the operation, and the clothing item IDsubjected to the operation of the clothing item cleaning appliance. Oncethe database receives the operating settings of the clothing itemcleaning appliance, the database can track how many times the clothingitem ID has been cleaned, and at what settings.

The processor can be part of the clothing item cleaning appliance, orthe processor can be part of a device separate from the clothing itemcleaning appliance such as a user device. The user device can be a cellphone, a tablet, a home system, etc. When the processor is part of thedevice separate from the clothing cleaning item appliance, the processorsends the retrieved care and content information to the clothing itemcleaning appliance.

FIG. 9 is a system to operate a clothing item cleaning applianceaccording to a care and content information of the clothing item. Thesystem includes a code reader 900, a receiving module 910, a databasecommunication module 920, an appliance communication module 930, and anoptional translation module 940. The code reader 900 obtains informationencoded in a clothing item including an identification (ID) of theclothing item. The code reader 900 can beam an RFID tag reader, an NFCtag reader, a camera, a Bluetooth receiver, Wi-Fi receiver, or a GPSreceiver. The clothing item ID can be printed on the clothing itemlabel, on the clothing item itself, or woven within the fabric of theclothing item.

The receiving module 910 receives the ID from the code reader and passesit to the database communication module 920. Database communicationmodule 920 retrieves from a database care and content information forthe clothing item, by sending the clothing item ID to the database, andin return receiving the care and content information associated with theID in the database. The care and content information can be washinginstructions, drying instructions, ironing instructions, color of theclothing item, materials contained in the clothing item, etc., asdescribed in this application.

Appliance communication module 930 automatically groups the clothingitem with multiple clothing items having care and content informationcorresponding to the care and content information of the clothing itemand operates a clothing item cleaning appliance in accordance with thecare and content information for the clothing item. The clothing itemcleaning appliance can be a washing machine, a drying machine, adry-cleaning machine, an ironing appliance, a steaming appliance, etc.The appliance communication module 930 can send instructions to theclothing item cleaning appliance and can receive information from theclothing item cleaning appliance. The instructions can contain operatingsettings of the clothing item cleaning appliance, and/or a time to startthe operation. The information received from the clothing item cleaningappliance can include the IDs of the clothing items inside the clothingitem cleaning appliance, and/or an operating settings of the completedcycle.

The appliance communication module 930 can detect that multiple clothingitems placed inside the clothing item cleaning appliance have disparatecare and content information. Upon detecting disparate care and contentinformation, the appliance communication module 930 can send anotification, to a user, that multiple clothing items placed inside theclothing item cleaning appliance have disparate care and contentinformation. The notification can be an error code displayed on the userdevice, or on the clothing item cleaning appliance. The appliancecommunication module 930 can allow the user to override the error, andspecify operating settings for the clothing item cleaning appliance.Further, upon detecting disparate care and content information, theappliance communication module 930 can determine a gentlest operatingsettings from the care and content information of multiple clothingitems, and operate the clothing item cleaning appliance according to thegentlest operating settings. For example, if care and contentinformation of one clothing item specify “machine wash cold”, while therest of the care and content information specify “machine wash hot,” theappliance communication module 930 can determine to operate the clothingitem cleaning appliance according to the “machine wash cold” operatingsettings.

The translation module 940 can determine a user preferred language basedon geolocation of the processor, or the user's language preference. Thetranslation module 940 can translate information presented to the userinto the user preferred language. When the translation module 940receives both the user's language preference and the geolocation of theprocessor, the translation module 940 translates received text accordingto the user's language preference.

The receiving module 910, the database communication module 920, theappliance communication module 930, and the translation module 940 canbe implemented on a processor 950 mounted within the clothing itemcleaning appliance. The receiving module 910, the database communicationmodule 920, the appliance communication module 930, and the translationmodule 940 can be implemented on a processor 950 mounted within a deviceseparate from the clothing item cleaning appliance, such as a cellphone, a tablet, a personal digital assistant, a home system, etc. Theprocessor 950 can be a microcontroller, a microprocessor, a programmablelogic device, special purpose hardware, etc.

FIG. 10 is a flowchart of a method to minimize consumption of textileused in manufacturing of a clothing item label. In step 1000 consumptionof textile used in manufacturing of a clothing item label is minimizedby a manufacturing system (such as a clothing item printer) encoding aclothing item identification (ID) of the clothing item within theclothing item. Instead of using hundreds of thousands of mile of textileto create clothing item labels containing information about washinginstructions, drying instructions, color, materials contained in theclothing item, etc., all the that is printed on the label can be aunique clothing item ID. The clothing item ID can be an alphanumericstring including letters numbers, special characters etc. The clothingitem ID can be “DK123M!” Further, the clothing item ID can be printed inink on the clothing item, or can be contained in the e-textile fibers ofthe clothing item. The clothing item ID can be encoded in at least oneof a radio-frequency identification (RFID) tag, a near fieldcommunication (NFC) tag, a quick response (QR) code, a barcode, aBluetooth transmitter, a Wi-Fi transmitter, a GPS transmitter, etc.

In addition, manufacturers of clothing item labels print the care andcontent label information in multiple languages because, at the time ofmanufacturing, manufacturers do not know where the clothing item withthe affixed label is going to be sold. Using the technology disclosedhere, translation is not necessary, because the translation of the careand content information is performed electronically based on thegeolocation of a device reading the clothing item ID, or based on auser's language preference.

In step 1010, the manufacturing system (such as computers used inmanufacturing) can send care and content information and the clothingitem ID to a database. The care and content information can include acontent of the clothing item, such as a material contained in theclothing item, and a percentage of the material contained in theclothing item.

In step 1020, the database can link the care and content information andthe clothing item ID within the database. In step 1030, the database canstore the care and content information and the clothing item ID in thedatabase. The database can be part of the manufacturing system.

A user device of a user can send a query to the database, where thequery includes the clothing item ID in the database. Upon receiving aquery from the user device, the database obtains a geolocation of theuser device querying the database. Based on the geolocation of the userdevice, the database sends a response to the query to the user device,the response translated into a language associated with the geolocationof the user device. Alternatively, the response can be translated into alanguage which the user indicates as the user's language preference. Theuser's language preference can override the language associated with thegeolocation of the device.

A processor associated with the database can determine care and contentinformation for the clothing item based on the material contained in theclothing item and the percentage of the material contained in theclothing item. For example, the processor can retrieve from the databasemultiple care and content information associated with multiple clothingitems. When at least one care and content information among multiplecare and content information is the same as the care and contentinformation of the clothing item, (i.e., materials and materialpercentages are the same), the processor determines that the careinformation for the clothing item should be the same as the careinformation for the matching item. For example, when the clothing itemcontains 80% cotton and 20% spandex, and the matching item also contains80% cotton and 20% spandex, the care information from the matching itemare associated with the care and content information of the clothingitem.

When none of multiple care and content information corresponds to thecontent of the clothing item, the processor can use machine learning todetermine the care and content information for the clothing item. Forexample, a machine learning system can determine a new set of careinformation based on multiple care and content information associatedwith multiple clothing items. The care information can include washinginstructions, drying instructions, ironing instructions, recommendationson which cleaning agents to use such as bleach, detergent, softener,etc. Once the machine learning determines the new set of careinformation, an administrator can clean the clothing item according tothe new set of care information and provide feedback to the machinelearning system whether the new set of care information performed wellin cleaning the clothing item. Based on the feedback from theadministrator, the machine learning system can positively or negativelyreinforce the method used in reaching the new set of care information.For example, if the machine learning system is a neural network, and thefeedback received from the administrator is positive feedback, thestrength of the connections between the neurons that produced the newset of care information can be strengthened. Conversely, if the machinelearning system is a neural network, and the feedback received from theadministrator is negative feedback, the strength of the connectionsbetween the neurons that produced the new set of care information can beweakened.

In another embodiment, when none of multiple care and contentinformation corresponds to the content of the clothing item, theprocessor can retrieve from the database multiple care and contentinformation associated with multiple clothing items. Among multiple careand content information, the processor can find a clothing item having acontent closest to the content of the clothing item. The closest contentcan be measured as root mean square deviation. For example, the clothingitem content is 70% cotton, 15% spandex, and 15% polyester. Firstclothing item among multiple clothing items has content of 80% cotton,and 20% spandex, while the second clothing item among multiple clothingitems has content of 70% cotton, 15% spandex and 15% nylon. The rootmean square distance to the first clothing item isSqrt((70-80)²+(15-20)²+(15-0)²)=18.7. The root mean square distance tothe second clothing item is Sqrt((70-70)²+(15-15)²+(15-0)²+(0-15)²)21.2. Thus, the content of the first clothing item is closest to thecontent of the clothing item under consideration. The processordetermines the care and content information of the clothing item underconsideration to correspond to care and content information of the firstclothing item. In another embodiment, instead of determining theclothing item having the content closest to the content of the clothingitem under consideration, the processor can linearly interpolate betweencare information of various clothing items. Once the processor hasdetermined the care information for the clothing item underconsideration, the processor updates the database to include thedetermined care and content information for the clothing item.

The processor can also track a number of times the clothing item hasbeen subjected to a clothing item cleaning appliance. For example theprocessor can track the number of times the clothing item has beenwashed, dried, dry-cleaned, ironed, steamed, etc. The processor can alsotrack the operating settings of the clothing item cleaning appliancethat the clothing item has been subjected to. When the number of timesthe clothing item has been subjected to the clothing item cleaningappliance exceeds a certain threshold, for example 50 times, theprocessor can offer to a user to purchase a new clothing item, identicalto the clothing item. The processor can perform the purchase after asingle click authorization from the user. In addition, when the clothingitem has been subjected to operating settings of the clothing itemcleaning appliance that are harsher than the operating settingsspecified in the clothing item label, the processor can also offer tothe user to purchase a new clothing item identical to the clothing item,after a single click authorization from the user.

The processor can track the content of multiple clothing items uponsale. The processor can determine the content with a highest number ofsales, and send a notification to a user that the determined content hasthe highest number of sales. The notification can be sent tomanufacturers, clothes designers, or end-users.

The processor can organize multiple clothing items associated with auser into multiple bins, where each bin among multiple bins includesclothing items that have the same care and content information. Theprocessor can send information contained in multiple bins to the user.

FIG. 11 is a system to encode clothing item ID within a clothing item.The system includes a communication layer 1100, a routing engine 1110, aclothing item analyzer 1120, a profile processor 1130, a suggestionengine 1140, and an optional translation module 1150. The communicationlayer 1100 communicates with an administrator, and a user.

The routing engine 1110 can distribute a message received at thecommunication layer 1100 to at least one of a clothing item analyzer1120, profile processor 1130, or a suggestion engine 1140. Further, therouting engine 1110 can send a message received from at least one of theclothing item analyzer 1120, the profile processor 1130, or thesuggestion engine 1140 to the communication layer 1100.

The clothing item analyzer 1120 can organize multiple clothing item IDsinto multiple categories based on care and content informationassociated with multiple clothing item IDs. For example, the clothingitem analyzer 1120 can organize multiple clothing item IDs into groupsbased on clothing item type, clothing item data, clothing item brand,etc. Information regarding clothing item type, clothing item data,clothing item brand, etc. is stored in the care and content informationassociated with the clothing item ID. Clothing item type can be shoes,purses, backpacks, hats, jackets, pants, skirts, dresses, etc. Based onthe data stored in the care and content information, the clothing itemanalyzer can analyze the data and create various reports, such asquantities of the clothing item produced for particular season. Clothingitem data can include information, such as country of origin, clothingitem manufacturer, quantity of the clothing item produced for aparticular season, style number, price, clothing item name, etc. Theclothing item analyzer 1120 can collect, process, and deliver theclothing item data to apparel companies, end users, logistics companies,customs, warehouses and retailers. Clothing item brand can include theapparel company or the brand name associated with the clothing item.

The profile processor 1130 organizes user information such as a useridentification and clothing item IDs associated with the user. Theprofile processor 1130 can analyze information about users, userhistory, user contact information, user wardrobe, user bins and how theuser organizes the washing bins, specifically, whether the washing binsare organized by garment type, by washing instructions, or by brands.The profile processor 1130 can also recommend to the user a clothingitem to purchase based on a clothing item brand associated with multipleclothing item IDs, clothing item type associated with multiple clothingitem IDs, and/or content information associated with multiple clothingitem IDs. Further, the profile processor 1130 can track a number oftimes the clothing item has been subjected to the clothing item cleaningappliance, and/or the operating settings of the clothing item cleaningappliance. For example, when the number of times the clothing item hasbeen subjected to the clothing item cleaning appliance exceeds a certainthreshold (e.g., 50 times), the profile processor 1130 can offer toperform a one click replacement purchase of the identical clothing itemfor the user. In another example, when the operating settings of theclothing item cleaning appliance are harsher than the recommended careinformation of the clothing item, the profile processor 1130 can offerto perform the one click replacement purchase of the identical clothingitem.

The suggestion engine 1140 analyzes care and content information ofmultiple clothing item IDs. Based on the analysis of care and contentinformation, the suggestion engine 1140 can provide suggestions aboutupdates to the care and content information. The suggestion engine 1140can retrieve from the database multiple care and content informationassociated with multiple clothing items, such as washing instructions,drying instructions, content information, etc. Among multiple care andcontent information, the suggestion engine 1140 can find a clothing itemhaving a content closest to the content of the clothing item. Theclosest content can be measured as root mean square deviation. Forexample, the clothing item content is 70% cotton, 15% spandex, and 15%polyester. First clothing item among multiple clothing items has contentof 80% cotton, and 20% spandex, while the second clothing item amongmultiple clothing items has content of 70% cotton, 15% spandex and 15%nylon. The root mean square distance to the first clothing item isSqrt((70-80)²+(15-20)²+(15-0)²)=18.7. The root mean square distance tothe second clothing item isSqrt((70-70)²+(15-15)²+(15-0)²+(0-15)²)=21.2. Thus, the content of theclothing item is closest to the content of the clothing item underconsideration. The suggestion engine 1140 determines the care andcontent information of the clothing item under consideration tocorrespond to care and content information of the first clothing item.In another embodiment, instead of determining the clothing item havingthe content closest to the content of the clothing item underconsideration, the suggestion engine 1140 can linearly interpolatebetween care information of various clothing items. Once the suggestionengine 1140 has determined the care information for the clothing itemunder consideration, the suggestion engine 1140 updates the database toinclude the determined care and content information for the clothingitem.

Further, the suggestion engine 1140 can use machine learning to updatecare information, or to create new care information for a clothing itemwhen materials and material percentage contents are not in the database.In addition, the suggestion engine 1140 can make suggestions toadministrators regarding the most popular materials sold in stores, asdescribed in this application. Once the machine learning determines thenew set of care information, an administrator can clean the clothingitem according to the new set of care information and provide feedbackto the machine learning system whether the new set of care informationperformed well in cleaning the clothing item. Based on the feedback fromthe administrator, the machine learning system can positively ornegatively reinforce the method used in reaching the new set of careinformation. For example, if the machine learning system is a neuralnetwork, and the feedback received from the administrator is positivefeedback, the strength of the connections between the neurons thatproduced the new set of care information can be strengthened.Conversely, if the machine learning system is a neural network, and thefeedback received from the administrator is negative feedback, thestrength of the connections between the neurons that produced the newset of care information can be weakened.

The translation module 1150 can receive user information including ageolocation of a user device, and/or the user's language preference.When the user's language preference is specified, the translation module1150 can translate the message received from at least one of theclothing item analyzer 1120, the profile processor 1130, or thesuggestion engine 1140 to the communication layer 1100 to a languageindicated in the user's language preference. The translation module 1150can then send the translated message to the communication layer 1100.When the user's language preference is not specified, the translationmodule 1150 can translate the message into a language associated withthe geolocation of the user device.

FIG. 12 is a flowchart of a method to increase accuracy of a care andcontent information associated with a clothing item label. In step 1200,a processor can increase the accuracy of care and content informationcontained in a clothing item label by causing a clothing item ID to beencoded within the clothing item. The clothing item ID can be encodedwithin an RFID tag, an NFC tag, a barcode, a QR code etc., as describedin this application.

In step 1210, the processor can send care and content information andthe clothing item ID to a database. The care and content information caninclude a content information of the clothing item such as a materialcontained in the clothing item and a percentage of the materialcontained in the clothing item, the care information of the clothingitem such as washing instructions. In step 1220, the processor can linkthe care and content information and the clothing item ID within thedatabase.

In step 1230, the processor can obtain an updated care and contentinformation of the clothing item. For example, the processor can obtainthe updated care and content information from administrator, correctinga prior error in the care and content information, or updating the careand content information based on newly available findings, such as whichdetergent is best for the clothing item.

In another example, the processor can obtain the updated care andcontent information by using machine learning to determine the careinformation for the clothing item based on multiple care and contentinformation associated with multiple clothing items, as described inthis application. In a third example, the processor can find a firstclothing item among multiple clothing items having a content informationclosest to the content information of the clothing item. Closest contentinformation can be measured using the root mean square deviation, asdescribed in this application. The processor can determine the careinformation to correspond to the care information of the first clothingitem. When there are multiple clothing items with the same root meansquare deviation from the clothing item under consideration, andmultiple clothing items have different care information, the processorcan either randomly select one of multiple clothing items, or theprocessor can linearly interpolate between the care information ofmultiple clothing items.

In step 1240, the processor can update the care and content informationof the clothing item in the database with the updated care and contentinformation of the clothing item. In step 1250, the processor, uponreceiving a query from a user device, can send the updated care andcontent information of the clothing item to the user device.

FIG. 13 shows a data structure storing clothing item information. Thedata structure 1300 is organized to enable efficient response to ananticipated query by defining multiple fields 1310, 1320, 1330, 1340(only four labeled for brevity) corresponding to the anticipated query.Further, the data structure 1300 enables an efficient update becausemultiple fields 1310, 1320 correspond to an anticipated update. Multiplefields 1310, 1320, 1330, 1340 can include: an ID field 1310 containingthe ID associated with the clothing item; a content field 1320containing the content information of the clothing item; a care fieldcontaining the care information of the clothing item; a place ofmanufacture field containing information about the place of manufactureof the clothing item; a type field containing information regarding thetype of garment such as shoes, purses, pants, a brand field; a sizefield containing information such as small, medium, large; a size chartfield containing information regarding measurements associated with thesmall, medium, large sizes; etc.

The most likely anticipated queries regarding a clothing item arecaptured in the name of the field such as content, care, size, etc.Further, the most likely updates to a clothing item are to the carefield. The updates can change the washing instructions, dryinginstructions, ironing instructions, or recommend particular cleaningagents to use, such as bleach, fabric softener, etc.

The data structure 1300 can include at least two optional fields 1330,1340. The field 1330 indicates whether the ID of the clothing item isunique to the clothing item. The indication whether the ID of theclothing item is unique can be obtained by reading the clothing item IDencoded within the clothing item. Additionally a database can store alookup table indicating which clothing item IDs are unique. If theclothing item ID is unique, a processor can dynamically create the field1340 to store history of the clothing item. For example, the history cancontain washing history of the clothing item, purchasing history of theclothing item, transportation history of the clothing item, distributionhistory of the clothing item, etc. Once the clothing item with a uniqueclothing item ID has been purchased, the processor stores the purchaseinformation in the history of field 1340 of the data structure 1300.Further, once the clothing item with the unique clothing item ID hasbeen washed, the processor stores the washing cycle information in thehistory field 1340 of the data structure 1300.

Any field 1310, 1320, 1330, 1340 in the data structure 1300 can includea nested data structure. In FIG. 13, the field 1320 contains a nesteddata structure, that is, the field 1320 itself is a data structure 1350.The data structure 1350 includes fields 1360, 1370, 1380, 1390 thatspecify content information of the clothing item. Field 1360 containsmaterials included in the clothing item, while the corresponding field1370 includes percentages of the respective materials included in theclothing item. Field 1380 specifies the color of the clothing item,while field 1390 specifies whether there is an application on theclothing item, such as beading, sequins, etc. In another embodiment, thecare field of the data structure 1300 can itself be a new data structurewhich contains fields corresponding to washing instructions, dryinginstructions, ironing instructions, preferred cleaning agents, etc.

FIG. 14 shows a data structure storing user information. The datastructure 1400 contains fields 1410, 1420, 1430. Field 1410 specifiesuser's name, field 1420 contains user's contact information, while field1430 contains a list of one or more of the user's clothing items 1440,1450. The user's clothing items can be represented as instances of thedata structure 1300 in FIG. 13.

FIGS. 15A-15B show various meta-data structures. The meta-data structure1500, 1540, are grouped according to the anticipated query to enableefficient response to the query. The meta-data structure 1500 in FIG.15A is three levels deep. The first level 1510 can contain the field ofthe data structure 1300 in FIG. 13, such as the brand. The second level1520, 1530 can contain various brands that the user owns, such asLevi's, Versace, etc. The third level contains clothing items associatedwith each of the brands 1520, 1530.

The meta-data structure 1540 in FIG. 15B is four levels deep. The firstlevel 1550 can contain the field of the data structure 1300 and FIG. 13,such as care information. The second level 1560, 1570 can containvarious subcategories of care information 1550, such as washinginstructions 1560 and drying instructions 1570. The third level cancontain various subcategories of washing instructions 1560 and dryinginstructions 1570. For example, washing instructions 1560 can besubdivided into categories 1580, 1590, 1505 according clothing itemcolor. Category 1580 corresponds to clothing items having a colorsimilar to red, category 1590 corresponds to clothing items having acolor similar to white, category 1505 corresponds to clothing items thatcan be washed with any color. The fourth level contains clothing itemsassociated with each of the clothing item colors 1580, 1590, 1505.

Washing instructions categories 1580, 1590, and 1505 can be displayed tothe user as bins containing items that should be washed together. Dryinginstructions category 1570 can also be displayed as a bin to the usercontaining items that should be dried together.

FIG. 16 is a flowchart of a method to efficiently respond to a queryregarding a clothing item. In step 1600 a processor receives a clothingitem identification (ID) of the clothing item, a content information ofthe clothing item, and a care information of the clothing item. Alongwith the clothing item ID, the processor can also receive an indicationthe clothing item ID is unique to the particular instance of theclothing item. By causing a clothing item identification (ID) to beencoded within the clothing item the consumption of textile used inmanufacturing of a clothing item label is minimized.

In step 1610, the processor creates, in a database, a data structureorganized to enable efficient response to an anticipated query bydefining multiple fields in the data structure corresponding to theanticipated query. Multiple fields include an ID field containing the IDassociated with the clothing item, a content field containing thecontent information of the clothing item, a care field containing thecare information of the clothing item, a place of manufacture fieldcontaining the place for the garment is manufactured, size fieldcontaining the size of the garment, a type field containing the type ofthe garment, brand field containing the brand of the garment, the sizechart field containing human measurements corresponding to varioussizes, etc. One or more of the fields in the data structure can containa second data structure, which also includes multiple fieldscorresponding to the anticipated query. An example of the second datastructure is content data structure 1350 in FIG. 13.

When the clothing item ID, received in step 1600, is unique, theprocessor can dynamically create a field in a data structure to storehistory associated with the clothing item ID. For example, the historycan contain wash history of the clothing item, purchasing history of theclothing item, transportation history the clothing item, distributionhistory of the clothing item, etc.

In step 1620, the processor, upon receiving a query, extracts a responseto the query from the data structure in the database by retrieving afield among multiple fields corresponding to the query.

The processor can also group the data structure into a meta-datastructure according to the anticipated query to enable efficientresponse to the query. Examples of the meta-data structures are shown inFIGS. 15A-15B.

The processor can also receive information uniquely associated with auser of the clothing item, and associate the user information and theclothing item ID in the database. Once the user information and theclothing item ID are associated, the processor can offer variousincentives to the user of the clothing item.

In one embodiment, once the user information and the clothing item IDare associated, the processor can identify a frequently recurring fieldin the data structure associated with the clothing item ID and the user,and offer a discount to the user for clothing items having propertiessubstantially similar to the frequently recurring field in the datastructure. For example, the processor can identify that in the wardrobeof user John Smith a frequently recurring feature is a white Cashmeresweater. The processor can then offer a discount on white Cashmeresweater on sale, a discount on Cashmere sweaters, or a discount onsweaters.

In another embodiment, once the user information and the clothing itemID are associated, if the clothing item ID is unique to the particularinstance of the clothing item, the processor can track a number of timesthe clothing item has been subjected to a clothing item cleaningappliance, and offer to the user to purchase a new clothing item basedon the number of times clothing item has been subjected to the clothingitem cleaning appliance. For example, if the clothing item has beenwashed more than 50 times, the processor can offer to the user topurchase with a single click the identical clothing item, with adifferent clothing item ID.

In a third embodiment, once the user information and the clothing itemID are associated, the processor can group multiple data structuresassociated with the user into multiple bins, wherein each bin includesclothing items that have the same care information. Subsequently, theprocessor can send information contained in multiple bins to the user.

The processor can also receive a query from a device, where the queryincludes the clothing item ID contained in the database. Upon receivingthe query from the user device, the processor can obtain a geolocationof the device querying the database. Based on the geolocation of theuser device, the processor can send a response to the query to thedevice, where the response is translated into a language associated withthe geolocation of the user device.

Given a new clothing item, the processor can determine the careinformation for the clothing item based on the content information ofthe clothing item. The content information of the clothing item caninclude material contained in the clothing item and the percentage ofthe material contained in the clothing item.

To determine care information for the new clothing item, in oneembodiment, the processor can retrieve from the database multiple datastructures associated with multiple clothing items, and use machinelearning to determine the care information for the clothing item basedon the care information of multiple clothing items. Once the machinelearning determines the new set of care information, an administratorcan clean the clothing item according to the new set of care informationand provide feedback to the machine learning system whether the new setof care information performed well in cleaning the clothing item. Basedon the feedback from the administrator, the machine learning system canpositively or negatively reinforce the method used in reaching the newset of care information. For example, if the machine learning system isa neural network, and the feedback received from the administrator ispositive feedback, the strength of the connections between the neuronsthat produced the new set of care information can be strengthened.Conversely, if the machine learning system is a neural network, and thefeedback received from the administrator is negative feedback, thestrength of the connections between the neurons that produced the newset of care information can be weakened.

To determine care information for the new clothing item, and anotherembodiment, the processor can retrieve from the database multiple datastructures associated with multiple clothing items, the processor canfind a first clothing item among multiple clothing items having thecontent information closest to the content information of the clothingitem than any other clothing item among multiple clothing items. Theclosest content can be measured as described in this application.Finally, the processor can assign the care information of the clothingitem to correspond to a care information of the first clothing item.

The processor can track multiple content information associated withmultiple clothing items when multiple clothing items have been sold. Theprocessor can determine a content information among multiple contentinformation with a highest number of sales. The processor can send anotification to the user that the determined content information has thehighest number of sales.

FIG. 17 is a flowchart of a method to enable efficient updates to careand content information of a clothing item. In step 1700, a processorreceives a clothing item identification (ID) associated with a clothingitem, a content information of the clothing item, and a care informationassociated with the clothing item.

In step 1710, the processor creates in a database a data structureorganized to enable an efficient update by defining multiple fields inthe data structure corresponding to an anticipated update. Multiplefields include an ID field containing the clothing item ID, a contentfield containing the content information of the clothing item, a carefield containing the care information of the clothing item, a place ofmanufacture field containing the place for the garment is manufactured,size field containing the size of the garment, a type field containingthe type of the garment, brand field containing the brand of thegarment, the size chart field containing human measurementscorresponding to various sizes, etc. One or more fields can contain asecond data structure as described in this application.

In step 1720, the processor determines an update to the data structure.In one embodiment, to determine the updates to the data structure, theprocessor retrieves from the database multiple content information ofmultiple clothing items and multiple care information of multipleclothing items. The processor uses machine learning to determine thecare information of the clothing item based on multiple care informationassociated with multiple clothing items, as described in thisapplication.

In another embodiment, to determine the updates to the data structure,the processor retrieves from the database multiple content informationof multiple clothing items and multiple care information of multipleclothing items. The processor finds a first clothing item among multipleclothing items having a content information closest to the contentinformation of the clothing item, as described in this application.Finally, the processor assigns the care information of the clothing itemto correspond to the care information of the first clothing item.

In step 1730, the processor updates the data structure by assigning avalue associated with the update to a field among multiple fields in thedata structure corresponding to the update.

FIG. 18 is a flowchart of a method to create a system to efficientlyrespond to an anticipated query. In step 1800 the communication layer1100 in FIG. 11 is configured to receive information from a user. Theinformation includes multiple clothing item identifications (IDs)associated with multiple clothing items, multiple content informationassociated with multiple clothing item IDs, and multiple careinformation associated with multiple clothing item IDs. Thecommunication layer 1100 can receive an indication that the clothingitem ID is unique to the clothing item.

In step 1810, the routing engine 1110 in FIG. 11 is configured todistribute a message, including the information received from the user,between the communication layer 1100 and at least one of the clothingitem analyzer 1120 in FIG. 11, the profile processor 1130 in FIG. 11, orthe suggestion engine 1140 in FIG. 11.

In step 1820, the clothing item analyzer 1120 is configured to organizemultiple clothing item IDs into multiple categories based on multiplecontent information and multiple care information associated withmultiple clothing item IDs. Multiple categories are organized to enableefficient response to a first anticipated query including a clothingitem ID, a clothing item type, or a clothing item brand. For example,the multiple categories can correspond to multiple data structures suchas data structures shown in FIGS. 13, 14, 15A, 15B. In a more specificexample, the clothing item analyzer 1120 can create a clothing item typecategory and organize it as shown in FIG. 15A, where the root node 1510corresponds to clothing item type, instead of clothing item brand.Clothing item type can be pants, a shirt, a sweater, etc. In anotherspecific example, the clothing item analyzer 1120 can create a brand andorganize it is shown in FIG. 15A. In a third specific example, theclothing item analyzer 1120 can create clothing item ID category andorganize it as shown in the data structure 1300 and FIG. 13. Further,when the clothing item ID is unique to the clothing item the clothingitem analyzer 1120 can store history associated with the clothing itemID, such as wash history, distribution history purchasing history, etc.into the appropriate data structure.

In step 1830, the profile processor 1130 in FIG. 11 is configured toorganize user information including a user identification and multipleclothing item IDs associated with the user into multiple profilecategories. Multiple profile categories are organized to enableefficient response to a second anticipated query, which includes theuser identification, a care information, and multiple clothing item IDsassociated with the user. For example, the multiple profile categoriescan correspond to the data structure 1400 in FIG. 14 and data structure1540 in FIG. 15B. The data structure 1400 includes user identification1410, 1420, and multiple clothing item IDs 1440, 1450 associated withthe user. The category containing the care information can correspond tothe meta-data structure 1540 in FIG. 15B. The root node 1550 of careinformation meta-data structure contains various care instructions suchas washing instructions 1560, drying instructions 1570, ironinginstructions (not shown), etc. The washing instructions 1560 can befurther subdivided according to color of the multiple clothing item IDs.Each node 1560, 1570 can be further subdivided, until the leaf nodesclothing item 1, clothing item 2, clothing item 3 contain the clothingitem data structure 1300 in FIG. 13. The profile processor 1130 cancreate the data structures described above.

To create the data structure 1400 in FIG. 14, the profile processor 1130in FIG. 11 can receive information uniquely associated with the user ofthe clothing item, such as purchaser name 1410 and purchaser contact1420, and associate the information uniquely associated with the user tothe clothing item ID 1440, 1450 to obtain the data structure 1400.Further, when the clothing item ID is unique to the clothing item theprofile processor 1130 can store history associated with the clothingitem ID, such as wash history, distribution history purchasing history,etc. into the appropriate data structure.

The suggestion engine 1140 in FIG. 11 can be configured to analyzemultiple care information and multiple content information. Based on theanalysis of care and content information, the suggestion engine 1140provides suggestions about updates to multiple care information. Thesuggestion engine 1140 can be configured to determine the careinformation for a clothing item based on a content information of theclothing item. The content information of the clothing item includesmaterial contained in the clothing item and a percentage of the materialcontained in the clothing item.

In one example, to determine the care information for the clothing item,the suggestion engine 1140 can be configured to retrieve multiplecontent information and multiple care information associated withmultiple clothing items, and use machine learning to determine the careinformation for the clothing item based on multiple content informationand multiple care information associated with multiple clothing items.Once the machine learning determines the new set of care information, anadministrator can clean the clothing item according to the new set ofcare information and provide feedback to the machine learning systemwhether the new set of care information performed well in cleaning theclothing item. Based on the feedback from the administrator, the machinelearning system can positively or negatively reinforce the method usedin reaching the new set of care information. For example, if the machinelearning system is a neural network, and the feedback received from theadministrator is positive feedback, the strength of the connectionsbetween the neurons that produced the new set of care information can bestrengthened. Conversely, if the machine learning system is a neuralnetwork, and the feedback received from the administrator is negativefeedback, the strength of the connections between the neurons thatproduced the new set of care information can be weakened.

In another example, to determine the care information for the clothingitem, the suggestion engine 1140 can be configured to retrieve multiplecare information and multiple content information associated withmultiple clothing items. The suggestion engine 1140 can find a firstclothing item among multiple clothing items having the contentinformation closest to the content information of the clothing item thanany other clothing item among multiple clothing items, as described inthis application. The suggestion engine 1140 can assign the careinformation of the clothing item to correspond to a care information ofthe first clothing item.

Further, the suggestion engine 1140 can track multiple contentinformation associated with multiple clothing items when multipleclothing items have been sold. The suggestion engine 1140 can determinea content information among multiple content information with a highestnumber of sales, and send a notification to the user that the determinedcontent information has the highest number of sales.

The profile processor 1130, the suggestion engine 1140, and/or theclothing item analyzer 1120 in FIG. 11 can identify a frequentlyrecurring field in the data structure associated with the clothing itemID and the user, and offer a discount to the user for clothing itemshaving properties substantially similar to the frequently recurringfield in the data structure. For example, the profile processor 1130,the suggestion engine 1140, and/or the clothing item analyzer 1120 canidentify that the user's wardrobe contains a lot of jeans, and offer tothe user discount for purchasing jeans in the future.

The profile processor 1130, the suggestion engine 1140, and/or theclothing item analyzer 1120 in FIG. 11 can track a number of times theclothing item has been subjected to a clothing item cleaning appliance,and offer to the user to purchase a new clothing item based on thenumber of times clothing item has been subjected to the clothing itemcleaning appliance. For example, once the profile processor 1130, thesuggestion engine 1140, and/or the clothing item analyzer 1120 determinethat the clothing item has been washed 50 times, the profile processor1130, the suggestion engine 1140, and/or the clothing item analyzer 1120can offer to the user to purchase a replacement clothing item with a oneclick purchase.

The profile processor 1130 in FIG. 11 can group multiple clothing itemIDs associated with the user into multiple bins. Each bin among multiplebins includes clothing items that have the same care information. Theprofile processor 1130 can send the information contained in multiplebins to the user.

A translation module 1150 in FIG. 11 can be configured to obtain ageolocation of a device sending a query to the communication layer.Based on the geolocation of the device, the translation module 1150 cansend a response to the query to the device, the response translated intoa language associated with the geolocation of the device.

FIG. 19 is a flowchart of a method to create a system to efficientlyupdate care and content information of a clothing item. In step 1900 thecommunication layer 1100 in FIG. 11 is configured to receive informationfrom a user, which includes multiple clothing item identifications (IDs)associated with multiple clothing items, multiple content informationassociated with multiple clothing item IDs, and multiple careinformation associated with multiple clothing item IDs.

In step 1910 the routing engine 1110 in FIG. 11 is configured todistribute a message, including the information received from the user,between the communication layer and at least one of the clothing itemanalyzer 1120 in FIG. 11, the suggestion engine 1140 in FIG. 11, or theprofile processor 1130 in FIG. 11.

In step 1920 the clothing item analyzer 1120 is configured to organizemultiple clothing item IDs into multiple categories based on multiplecontent information and multiple care information associated withmultiple clothing item IDs. Multiple categories include a data structureorganized to enable an efficient update by defining multiple fields inthe data structure corresponding to an anticipated update. Multiplefields can include an ID field containing a clothing item ID, and a carefield containing a care information for the clothing item. For example,the multiple categories can correspond to multiple data structures suchas data structures shown in FIGS. 13, 14, 15A, 15B. In a more specificexample, the clothing item analyzer 1120 can create clothing item IDcategory and organize it as shown in the data structure 1300 in FIG. 13.

In step 1930, the suggestion engine 1140 is configured to determine thecare information for a clothing item based on the content information ofthe clothing item, multiple content information associated with multipleclothing items and multiple care information associated with multipleclothing items. The content information of the clothing item includesmaterial contained in the clothing item and a percentage of the materialcontained in the clothing item.

The suggestion engine 1140 can determine the care information byretrieving multiple content information of multiple clothing items andmultiple care information of multiple clothing items, and by usingmachine learning to determine the care information of the clothing itembased on multiple care information associated with multiple clothingitems, as described in this application.

The suggestion engine 1140 can determine the care information byretrieving multiple content information of multiple clothing items andmultiple care information of multiple clothing items. Next, thesuggestion engine 1140 can find a first clothing item among multipleclothing items having a content information closest to the contentinformation of the clothing item, as described in this application.Finally, the suggestion engine 1140 can assign the care information ofthe clothing item to correspond to the care information of the firstclothing item.

Once the suggestion engine 1140 determines the care information of theclothing item, the clothing item analyzer 1120, the profile processor1130, and the suggestion engine 1140 can update their respective datastructures by assigning the new care information to the correspondingcare fields.

Computer

FIG. 20 is a diagrammatic representation of a machine in the exampleform of a computer system 2000 within which a set of instructions, forcausing the machine to perform any one or more of the methodologies ormodules discussed herein, may be executed.

The computer system 2000 can be a part of the clothing item cleaningappliance, a device separate from the clothing item cleaning appliance,such as a cell phone, and/or the system in FIG. 11.

When the computer system 2000 is part of the clothing item cleaningappliance, a processor of the computer system 2000 can receive theclothing item ID, and retrieve from a database care and contentinformation associated with the clothing item ID. The display of thecomputer system 2000 can display an error code, when clothing items withdisparate care and content information are placed within the clothingitem cleaning appliance.

When the computer system 2000 is part of the device separate from theclothing item cleaning appliance, the processor of the computer system2000 can receive the clothing item ID, communicate with a databasestoring the care and content information, and operate the clothing itemcleaning appliance. The display of the computer system 2000 cancommunicate with a user by, for example, displaying a user's wardrobearranged in clothing bins according to care information.

When the computer system 2000 is part of the system shown in FIG. 11,the computer system 2000 can contain one or more processors implementingthe communication layer 1100, the routing engine 1110, the clothing itemanalyzer 1120, the profile processor 1130, the suggestion engine 1140,and the translation module 1150, in software and/or in hardware.

The various databases disclosing this application can exist in the mainmemory, in the nonvolatile memory, or in the machine-readable storagemedium of the computer system 2000. The communication between variouselements disclosed in this application can be performed via the networkof the computer system 2000.

In the example of FIG. 20, the computer system 2000 includes aprocessor, memory, non-volatile memory, and an interface device. Variouscommon components (e.g., cache memory) are omitted for illustrativesimplicity. The computer system 2000 is intended to illustrate ahardware device on which any of the components described in the exampleof FIGS. 1-19 (and any other components described in this specification)can be implemented. The computer system 2000 can be of any applicableknown or convenient type. The components of the computer system 2000 canbe coupled together via a bus or through some other known or convenientdevice.

This disclosure contemplates the computer system 2000 taking anysuitable physical form. As example and not by way of limitation,computer system 2000 may be an embedded computer system, asystem-on-chip (SOC), a single-board computer system (SBC) (such as, forexample, a computer-on-module (COM) or system-on-module (SOM)), adesktop computer system, a laptop or notebook computer system, aninteractive kiosk, a mainframe, a mesh of computer systems, a mobiletelephone, a personal digital assistant (PDA), a server, or acombination of two or more of these. Where appropriate, computer system2000 may include one or more computer systems 2000; be unitary ordistributed; span multiple geolocations; span multiple machines; orreside in a cloud, which may include one or more cloud components in oneor more networks. Where appropriate, one or more computer systems 2000may perform without substantial spatial or temporal limitation one ormore steps of one or more methods described or illustrated herein. As anexample and not by way of limitation, one or more computer systems 2000may perform in real time or in batch mode one or more steps of one ormore methods described or illustrated herein. One or more computersystems 2000 may perform at different times or at different geolocationsone or more steps of one or more methods described or illustratedherein, where appropriate.

The processor may be, for example, a conventional microprocessor such asan Intel Pentium microprocessor or Motorola power PC microprocessor. Oneof skill in the relevant art will recognize that the terms“machine-readable (storage) medium” or “computer-readable (storage)medium” include any type of device that is accessible by the processor.

The memory is coupled to the processor by, for example, a bus. Thememory can include, by way of example but not limitation, random accessmemory (RAM), such as dynamic RAM (DRAM) and static RAM (SRAM). Thememory can be local, remote, or distributed.

The bus also couples the processor to the non-volatile memory and driveunit. The non-volatile memory is often a magnetic floppy or hard disk, amagnetic-optical disk, an optical disk, a read-only memory (ROM), suchas a CD-ROM, EPROM, or EEPROM, a magnetic or optical card, or anotherform of storage for large amounts of data. Some of this data is oftenwritten, by a direct memory access process, into memory during executionof software in the computer 2000. The non-volatile storage can be local,remote, or distributed. The non-volatile memory is optional becausesystems can be created with all applicable data available in memory. Atypical computer system will usually include at least a processor,memory, and a device (e.g., a bus) coupling the memory to the processor.

Software is typically stored in the non-volatile memory and/or the driveunit. Indeed, storing and entire large program in memory may not even bepossible. Nevertheless, it should be understood that for software torun, if necessary, it is moved to a computer readable geolocationappropriate for processing, and for illustrative purposes, thatgeolocation is referred to as the memory in this paper. Even whensoftware is moved to the memory for execution, the processor willtypically make use of hardware registers to store values associated withthe software, and local cache that, ideally, serves to speed upexecution. As used herein, a software program is assumed to be stored atany known or convenient geolocation (from non-volatile storage tohardware registers) when the software program is referred to as“implemented in a computer-readable medium.” A processor is consideredto be “configured to execute a program” when at least one valueassociated with the program is stored in a register readable by theprocessor.

The bus also couples the processor to the network interface device. Theinterface can include one or more of a modem or network interface. Itwill be appreciated that a modem or network interface can be consideredto be part of the computer system 2000. The interface can include ananalog modem, isdn modem, cable modem, token ring interface, satellitetransmission interface (e.g., “direct PC”), or other interfaces forcoupling a computer system to other computer systems. The interface caninclude one or more input and/or output devices. The I/O devices caninclude, by way of example but not limitation, a keyboard, a mouse orother pointing device, disk drives, printers, a scanner, and other inputand/or output devices, including a display device. The display devicecan include, by way of example but not limitation, a cathode ray tube(CRT), liquid crystal display (LCD), or some other applicable known orconvenient display device. For simplicity, it is assumed thatcontrollers of any devices not depicted in the example of FIG. 20 residein the interface.

In operation, the computer system 2000 can be controlled by operatingsystem software that includes a file management system, such as a diskoperating system. One example of operating system software withassociated file management system software is the family of operatingsystems known as Windows® from Microsoft Corporation of Redmond, Wash.,and their associated file management systems. Another example ofoperating system software with its associated file management systemsoftware is the Linux™ operating system and its associated filemanagement system. The file management system is typically stored in thenon-volatile memory and/or drive unit and causes the processor toexecute the various acts required by the operating system to input andoutput data and to store data in the memory, including storing files onthe non-volatile memory and/or drive unit.

Some portions of the detailed description may be presented in terms ofalgorithms and symbolic representations of operations on data bitswithin a computer memory. These algorithmic descriptions andrepresentations are the means used by those skilled in the dataprocessing arts to most effectively convey the substance of their workto others skilled in the art. An algorithm is here, and generally,conceived to be a self-consistent sequence of operations leading to adesired result. The operations are those requiring physicalmanipulations of physical quantities. Usually, though not necessarily,these quantities take the form of electrical or magnetic signals capableof being stored, transferred, combined, compared, and otherwisemanipulated. It has proven convenient at times, principally for reasonsof common usage, to refer to these signals as bits, values, elements,symbols, characters, terms, numbers, or the like.

It should be borne in mind, however, that all of these and similar termsare to be associated with the appropriate physical quantities and aremerely convenient labels applied to these quantities. Unlessspecifically stated otherwise as apparent from the following discussion,it is appreciated that throughout the description, discussions utilizingterms such as “processing” or “computing” or “calculating” or“determining” or “displaying” or “generating” or the like, refer to theaction and processes of a computer system, or similar electroniccomputing device, that manipulates and transforms data represented asphysical (electronic) quantities within the computer system's registersand memories into other data similarly represented as physicalquantities within the computer system memories or registers or othersuch information storage, transmission or display devices.

The algorithms and displays presented herein are not inherently relatedto any particular computer or other apparatus. Various general purposesystems may be used with programs in accordance with the teachingsherein, or it may prove convenient to construct more specializedapparatus to perform the methods of some embodiments. The requiredstructure for a variety of these systems will appear from thedescription below. In addition, the techniques are not described withreference to any particular programming language, and variousembodiments may thus be implemented using a variety of programminglanguages.

In alternative embodiments, the machine operates as a standalone deviceor may be connected (e.g., networked) to other machines. In a networkeddeployment, the machine may operate in the capacity of a server or aclient machine in a client-server network environment, or as a peermachine in a peer-to-peer (or distributed) network environment.

The machine may be a server computer, a client computer, a personalcomputer (PC), a tablet PC, a laptop computer, a set-top box (STB), apersonal digital assistant (PDA), a cellular telephone, an iPhone, aBlackberry, a processor, a telephone, a web appliance, a network router,switch or bridge, or any machine capable of executing a set ofinstructions (sequential or otherwise) that specify actions to be takenby that machine.

While the machine-readable medium or machine-readable storage medium isshown in an exemplary embodiment to be a single medium, the term“machine-readable medium” and “machine-readable storage medium” shouldbe taken to include a single medium or multiple media (e.g., acentralized or distributed database, and/or associated caches andservers) that store the one or more sets of instructions. The term“machine-readable medium” and “machine-readable storage medium” shallalso be taken to include any medium that is capable of storing, encodingor carrying a set of instructions for execution by the machine and thatcause the machine to perform any one or more of the methodologies ormodules of the presently disclosed technique and innovation.

In general, the routines executed to implement the embodiments of thedisclosure, may be implemented as part of an operating system or aspecific application, component, program, object, module or sequence ofinstructions referred to as “computer programs.” The computer programstypically comprise one or more instructions set at various times invarious memory and storage devices in a computer, and that, when readand executed by one or more processing units or processors in acomputer, cause the computer to perform operations to execute elementsinvolving the various aspects of the disclosure.

Moreover, while embodiments have been described in the context of fullyfunctioning computers and computer systems, those skilled in the artwill appreciate that the various embodiments are capable of beingdistributed as a program product in a variety of forms, and that thedisclosure applies equally regardless of the particular type of machineor computer-readable media used to actually effect the distribution.

Further examples of machine-readable storage media, machine-readablemedia, or computer-readable (storage) media include but are not limitedto recordable type media such as volatile and non-volatile memorydevices, floppy and other removable disks, hard disk drives, opticaldisks (e.g., Compact Disk Read-Only Memory (CD ROMS), Digital VersatileDisks, (DVDs), etc.), among others, and transmission type media such asdigital and analog communication links.

In some circumstances, operation of a memory device, such as a change instate from a binary one to a binary zero or vice-versa, for example, maycomprise a transformation, such as a physical transformation. Withparticular types of memory devices, such a physical transformation maycomprise a physical transformation of an article to a different state orthing. For example, but without limitation, for some types of memorydevices, a change in state may involve an accumulation and storage ofcharge or a release of stored charge. Likewise, in other memory devices,a change of state may comprise a physical change or transformation inmagnetic orientation or a physical change or transformation in molecularstructure, such as from crystalline to amorphous or vice versa. Theforegoing is not intended to be an exhaustive list in which a change instate for a binary one to a binary zero or vice-versa in a memory devicemay comprise a transformation, such as a physical transformation.Rather, the foregoing is intended as illustrative examples.

A storage medium typically may be non-transitory or comprise anon-transitory device. In this context, a non-transitory storage mediummay include a device that is tangible, meaning that the device has aconcrete physical form, although the device may change its physicalstate. Thus, for example, non-transitory refers to a device remainingtangible despite this change in state.

REMARKS

The language used in the specification has been principally selected forreadability and instructional purposes, and it may not have beenselected to delineate or circumscribe the inventive subject matter. Itis therefore intended that the scope of the invention be limited not bythis Detailed Description, but rather by any claims that issue on anapplication based hereon. Accordingly, the disclosure of variousembodiments is intended to be illustrative, but not limiting, of thescope of the embodiments, which is set forth in the following claims.

1. A method comprising: retrieving, by a processor, from a database aplurality of care information associated with a plurality of clothingitem identifications (IDs), a plurality of content informationassociated with the plurality of clothing item IDs and the plurality ofclothing item IDs associated with a plurality of clothing items;creating, by the processor, an update comprising an update to a careinformation of a clothing item in the plurality of clothing item or anupdate to a content information of the clothing item in the plurality ofclothing item; sending, by the processor, from the database the updateto a clothing item cleaning appliance thereby reducing consumption ofnon-sustainable materials contained in a clothing label by avoidingre-creation of the clothing label; and operating, by the processor, theclothing item cleaning appliance in accordance with the care informationassociated with a clothing item.
 2. The method of claim 1, said creatingthe update comprising: creating, by the processor, the update increasinglongevity of the clothing item in the plurality of clothing items byanalyzing the plurality of care information and the plurality of contentinformation.
 3. The method of claim 1, said creating the updatecomprising: determining, by the processor, the care information of aclothing item based on the content information of the clothing item, thecontent information of the clothing item comprising material containedin the clothing item and a percentage of the material contained in theclothing item.
 4. The method of claim 3, said creating the updatecomprising: retrieving, by the processor, the plurality of contentinformation and the plurality of care information associated with theplurality of clothing item IDs; and using, by the processor, machinelearning to determine the care information for the clothing item basedon the plurality of content information and the plurality of careinformation associated with the plurality of clothing items.
 5. Themethod of claim 3, said creating the update comprising: retrieving, bythe processor, the plurality of care information and the plurality ofcontent information associated with the plurality of clothing items;finding, by the processor, a first clothing item in the plurality ofclothing items having the content information closest to the contentinformation of the clothing item than any other clothing item in theplurality of clothing items; and assigning, by the processor, the careinformation of the clothing item to correspond to a care information ofthe first clothing item.
 6. The method of claim 1, comprising: tracking,by the processor, the plurality of content information associated withthe plurality of clothing items when the plurality of clothing itemshave been sold; determining, by the processor, the content informationin the plurality of content information with a highest number of sales;and sending, by the processor, a notification to a user that thedetermined content information has the highest number of sales.
 7. Themethod of claim 1, comprising storing, by the processor, a history ofthe clothing item in the database, the history comprising a washinghistory of the clothing item, or a purchasing history of the clothingitem.
 8. The method of claim 1, comprising: determining, by theprocessor, a type of clothing frequently purchased by a user; andoffering, by the processor, a discount to the user for clothing itemshaving properties substantially similar to the type of clothingfrequently purchased by the user.
 9. The method of claim 1, comprising:tracking, by the processor, a number of times the clothing item has beensubjected to the clothing item cleaning appliance; and offering, by theprocessor, to a user to purchase a new clothing item based on the numberof times clothing item has been subjected to the clothing item cleaningappliance.
 10. The method of claim 1, comprising: grouping, by theprocessor, the plurality of clothing item IDs associated with a userinto a plurality of bins, wherein each bin in the plurality of binscomprises clothing items that have the same care information; andsending, by the processor, information contained in the plurality ofbins to the user.
 11. The method of claim 1, comprising: determining, bythe processor, a user preferred language based on geolocation of a user,or a user's language preference; and translating, by the processor,information sent to the user into the user preferred language.
 12. Amethod comprising: retrieving, by a processor, from a database aplurality of care information and a plurality of content informationassociated with a plurality of clothing item identifications (IDs)coupled to a plurality of clothing items, a content information in theplurality of content information comprising a percentage of a materialcontained in a clothing item in the plurality of clothing items, and acare information in the plurality of care information comprising aclothing item cleaning appliance instructions; creating, by theprocessor, a suggestion comprising an increase in a longevity of theclothing item by analyzing the plurality of care information and theplurality of content information; and sending, by the processor, fromthe database the suggestion to a user, thereby reducing consumption ofnon-sustainable materials contained in a clothing label by avoidingre-creation of the clothing label.
 13. The method of claim 12, saidcreating the suggestion comprising: determining, by the processor, theincrease in the longevity of the clothing item when the clothing item issubjected to a treatment different from the care information; andcreating, by the processor, an update to the care information comprisingthe treatment different from the care information.
 14. The method ofclaim 12, said creating the suggestion comprising: determining, by theprocessor, a popular clothing item based on an amount of salesassociated with the plurality of clothing items; and creating, by theprocessor, the suggestion comprising a notification to the usercomprising the popular clothing item.
 15. The method of claim 12, saidcreating the suggestion comprising: determining, by the processor, apopular material associated with the clothing item based on an amount ofsales associated with the plurality of clothing items; and creating, bythe processor, a notification to the user comprising the popularmaterial.
 16. The method of claim 12, said creating the suggestioncomprising: tracking, by the processor, a number of times the clothingitem has been subjected to a clothing item cleaning appliance; and whenthe number of times exceeds a predefined threshold, creating, by theprocessor, an offer to purchase an identical clothing item.
 17. Themethod of claim 12, said creating the suggestion comprising: tracking,by the processor, an operating setting of a clothing item cleaningappliance applied to the clothing item; and when the operating settingof the clothing item cleaning appliance are harsher than the careinformation of the clothing item cleaning appliance, creating, by theprocessor, the suggestion comprising an offer to purchase identicalclothing item.
 18. A system comprising: a database storing a pluralityof care information associated with a plurality of clothing items, aplurality of content information associated with the plurality ofclothing items and a plurality of clothing item identifications (ID)associated with the plurality of clothing items; a clothing itemanalyzer organizing the plurality of clothing item IDs into a pluralityof categories based on the plurality of care information and theplurality of content information associated with the plurality ofclothing item IDs; a profile processor organizing user informationcomprising a user identification and clothing item IDs associated with auser; a suggestion engine creating an update comprising an update to acare information of a clothing item in the plurality of clothing itemsor an update to a content information of the clothing item in theplurality of clothing items; and a communication layer sending theupdate to a clothing item cleaning appliance thereby reducingconsumption of non-sustainable materials contained in a clothing labelby avoiding re-creation of the clothing label, and causing the clothingitem cleaning appliance to operate in accordance with the update. 19.The system of claim 18, the suggestion engine: determining the careinformation of the clothing item based on the content information of theclothing item, the content information of the clothing item comprisingmaterial contained in the clothing item and a percentage of the materialcontained in the clothing item.
 20. The system of claim 18, thesuggestion engine: creating, using a machine learning system, the updateto the care information, the update to the care information increasing alongevity of a clothing item in the plurality of clothing items byanalyzing the plurality of care information and the plurality of contentinformation.
 21. The system of claim 18, comprising a digital component,attached to a clothing item, comprising an ID of the clothing item, thedigital component coated in a waterproof and heatproof material.
 22. Thesystem of claim 18, the clothing item analyzer organizing the pluralityof clothing item IDs into the plurality of categories based on aclothing item brand, the content information, the care information, or aclothing item type.
 23. The system of claim 18, the profile processor:receiving user information comprising the user identification, theplurality of clothing item IDs associated with the user; and organizingthe plurality of clothing item IDs into the plurality of categoriesbased on the care information.
 24. The system of claim 18, the profileprocessor: receiving user information comprising the useridentification, the plurality of clothing item IDs associated with theuser; and recommending to the user a clothing item to purchase based ona clothing item brand associated with the plurality of clothing itemIDs, a clothing item type associated with the plurality of clothing itemIDs, and the content information associated with the plurality ofclothing item IDs.
 25. The system of claim 18, the suggestion engine:receiving a second content information not contained in the plurality ofcontent information of the plurality of clothing item IDs, the secondcontent information associated with a second clothing item ID; andrecommending a care information of the second clothing item ID based onthe plurality of care information and the plurality of contentinformation associated with the plurality of clothing item IDs.
 26. Thesystem of claim 18, the suggestion engine: tracking the plurality ofcontent information associated with the plurality of clothing items whenthe plurality of clothing items have been sold; determining the contentinformation in the plurality of content information with a highestnumber of sales; and sending a notification to the user that thedetermined content information has the highest number of sales.
 27. Thesystem of claim 18, the database storing a history of the clothing itemin the database, the history comprising a washing history of theclothing item, or a purchasing history of the clothing item.
 28. Thesystem of claim 18, the profile processor: determining a type ofclothing frequently purchased by the user; and offering a discount tothe user for clothing items having properties substantially similar tothe type of clothing frequently purchased by the user.
 29. The system ofclaim 18, comprising a translation module: receiving a languageindication based on a geolocation of a user device, or a user's languagepreference; translating a user message to a language associated with thelanguage indication; and sending the translated user message to theuser.
 30. The system of claim 18, a routing engine distributing amessage between the communication layer and at least one of the clothingitem analyzer, the profile processor, or the suggestion engine.